Citation

Please cite TCGAbiolinks package:

• “TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.” Nucleic acids research (2015): gkv1507. (Colaprico, Antonio and Silva, Tiago C. and Olsen, Catharina and Garofano, Luciano and Cava, Claudia and Garolini, Davide and Sabedot, Thais S. and Malta, Tathiane M. and Pagnotta, Stefano M. and Castiglioni, Isabella and Ceccarelli, Michele and Bontempi, Gianluca and Noushmehr, Houtan 2016)

Related publications to this package:

• “TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages”. F1000Research 10.12688/f1000research.8923.1 (Silva, TC and Colaprico, A and Olsen, C and D’Angelo, F and Bontempi, G and Ceccarelli, M and Noushmehr, H 2016)

getDataTCGA: Searching by cancer type and data type [Gene Expression]

The user can query and download the cancer types supported by TCGA, using the function getDataTCGA: In this example we used LUAD gene expression data with only 10 samples to reduce time downloading.

dataFilt <- getDataTCGA(cancerType = "LUAD", 
                          dataType = "Gene expression",
                          directory = "data",
                          nSample = 10)

getDataTCGA: Searching by cancer type and data type [Methylation]

The user can also query and download methylation data using the function getDataTCGA:

setwd("~/Dropbox/IB2_postdoc/Github/Moonlight/")
dataFilt <- getDataTCGA(cancerType = "TCGA-BRCA", 
                          dataType = "Methylation", 
                          directory = "data",nSample = 5)

getDataGEO: Searching by cancer type and data type [Gene Expression]

The user can query and download the cancer types supported by GEO, using the function getDataGEO:

dataFilt <- getDataGEO(GEOobject = "GSE20347",platform = "GPL571")

dataFilt <- getDataGEO(TCGAtumor = "ESCA")

DPA: Differential Phenotype Analysis

Differential Phenotype analysis is able to identify genes or probes that are significantly different between two phenotypes such as normal vs. tumor, or normal vs. stageI, normal vs. molecular subtype.

For gene expression data, DPA is running a differential expression analysis (DEA) to identify differentially expressed genes (DEGs) using the TCGAanalyze_DEA function from .

For methylation data DPA is running a differentially methylated regions analysis (DMR) to identify differentially methylated CpG sites using the TCGAanalyze_DMR the TCGAanalyze_DMR function from .

dataDEGs <- DPA(dataFilt = dataFilt,
                dataType = "Gene expression")

For gene expression data, DPA dealing with GEO data is running a differential expression analysis (DEA) to identify differentially expressed genes (DEGs) using to the eBayes and topTable functions from .

DataAnalysisGEO<- "../GEO_dataset/"
i<-5

cancer <- GEO_TCGAtab$Cancer[i]
cancerGEO <- GEO_TCGAtab$Dataset[i]
cancerPLT <-GEO_TCGAtab$Platform[i]
fileCancerGEO <- paste0(cancer,"_GEO_",cancerGEO,"_",cancerPLT, ".RData")

dataFilt <- getDataGEO(TCGAtumor = cancer)

GEOdegs <- DPA(dataConsortium = "GEO",
               gset = dataFilt ,
               colDescription = "title",
               samplesType  = c(GEO_TCGAtab$GEO_Normal[i],
                                GEO_TCGAtab$GEO_Tumor[i]),
               fdr.cut = 0.01,
               logFC.cut = 1,
               gsetFile = paste0(DataAnalysisGEO,fileCancerGEO))

We can visualize those differentially expressed genes (DEGs) with a volcano plot using the TCGAVisualize_volcano function from .

library(TCGAbiolinks)
TCGAVisualize_volcano(DEGsmatrix$logFC, DEGsmatrix$FDR,
                      filename = "DEGs_volcano.png",
                      x.cut = 7,
                      y.cut = 10^-5,
                      names = rownames(dataDEGs),
                      color = c("black","red","dodgerblue3"),
                      names.size = 2,
                      xlab = " Gene expression fold change (Log2)",
                      legend = "State",
                      title = "Volcano plot (Normal NT vs Tumor TP)",
                      width = 10)

The figure resulted from the code above is shown below:

LPA: Literature Phenotype Analysis

The user can perform a literature phenotype analysis using the function LPA.

data(DEGsmatrix)

DiseaseListNew <- list()
BPselected <- c("apoptosis","proliferation of cells")

for (i in 1:length(BPselected)){
  BPannotations <- DiseaseList[[which(names(DiseaseList) == BPselected[i])]]$ID
  dataLPA <- LPA(dataDEGs = DEGsmatrix,
               BP =  BPselected[i],
               BPlist = BPannotations)
  DiseaseListNew[[length(DiseaseListNew)+1]] <- dataLPA
  names(DiseaseListNew)[[i]] <- BPselected[i]
}

FEA: Functional Enrichment Analysis

The user can perform a functional enrichment analysis using the function FEAcomplete. For each DEG in the gene set a z-score is calculated. This score indicates how the genes act in the gene set.

dataFEA <- FEA(DEGsmatrix = DEGsmatrix)

The output can be visualized with a FEA plot.

FEAplot: Functional Enrichment Analysis Plot

The user can plot the result of a functional enrichment analysis using the function plotFEA. A negative z-score indicates that the process’ activity is decreased. A positive z-score indicates that the process’ activity is increased.

plotFEA(dataFEA = dataFEA, plotNAME = "FEAplot", height = 20, width = 10)

The figure generated by the above code is shown below:

GRN: Gene Regulatory Network

The user can perform a gene regulatory network analysis using the function GRN which infers the network using the parmigene package.

dataGRN <- GRN(TFs = rownames(DEGsmatrix)[1:10], normCounts = dataFilt,
                   nGenesPerm = 10,kNearest = 3,nBoot = 10)

URA: Upstream Regulator Analysis

The user can perform upstream regulator analysis using the function URA. This function is applied to each DEG in the enriched gene set and its neighbors in the GRN.

data(dataGRN)
data(DEGsmatrix)
dataURA <- URA(dataGRN = dataGRN,
               DEGsmatrix = DEGsmatrix,
               BPname = NULL)

PRA: Pattern Regognition Analysis

The user can retrieve TSG/OCG candidates using either selected biological processes or a random forest classifier trained on known COSMIC OCGs/TSGs.

dataDual <- PRA(dataURA = dataURA,
                          BPname = c("apoptosis","proliferation of cells"),
                          thres.role = 0)

The figure generated by the above code is shown below:

Moonlight Replication in Cosmic cancer genes:

Running moonlight with a list of already validated TSG and OCG.

CosmicGenes <- c(knownDriverGenes$OCG, knownDriverGenes$TSG)
   
dataFilt <- getDataTCGA(cancerType = "BRCA", 
                          dataType = "Gene expression",
                          directory = "data",
                          nSample = 10)

dataDEGs <- DPA(dataFilt = dataFilt,
                dataType = "Gene expression")

dataFEA <- FEA(DEGsmatrix = dataDEGs)

dataGRN <- GRN(TFs = CosmicGenes, 
               DEGsmatrix = dataDEGs,
               DiffGenes = TRUE,
               normCounts = dataFilt)

dataURA <-URA(dataGRN = dataGRN, 
              DEGsmatrix = dataDEGs, 
              BPname = c("apoptosis",
                         "proliferation of cells"))

dataDual <-PRA(dataURA = dataURA, 
               BPname = c("apoptosis",
                          "proliferation of cells"),
               thres.role = 1)

plotNetworkHive: GRN hive visualization taking into account Cosmic cancer genes

In the following plot the nodes are separated into three groups: known tumor suppressor genes (yellow), known oncogenes (green) and the rest (gray).

data(knownDriverGenes)
plotNetworkHive(dataGRN, knownDriverGenes, 0.75)

Introduction

This vignette shows a complete workflow of the ‘MoonlightR’ package. The code is divided in 4 case study:

• 1. Downstream analysis LUAD using RNA expression data
• 2. Expression pipeline Pan Cancer with five cancer types
• 3. Expression pipeline with stages I II III IV (BRCA)
• 4. Subtypes identification with cancer genes (KIRC)

Case study n. 1: Downstream analysis LUAD

dataFilt <- getDataTCGA(cancerType = "LUAD", 
                          dataType = "Gene expression",
                          directory = "data",
                          nSample = 10)

DEGsmatrix <- DPA(dataFilt = dataFilt,
                dataType = "Gene expression")

dataFEA <- FEA(DEGsmatrix = dataDEGs)

dataGRN <- GRN(TFs = rownames(dataDEGs)[1:1000], 
               DEGsmatrix = dataDEGs,
               DiffGenes = TRUE,
               normCounts = dataFilt)
dataURA <-URA(dataGRN = dataGRN, 
              DEGsmatrix = DEGsmatrix, 
              BPname = c("apoptosis",
                         "proliferation of cells"))

dataDual <-PRA(dataURA = dataURA, 
               BPname = c("apoptosis",
                          "proliferation of cells"),
               thres.role = 1)

CancerGenes <- list("TSG"=names(dataDual$TSG), "OCG"=names(dataDual$OCG))

plotURA: Upstream regulatory analysis plot

The user can plot upstream regulatory analysis using the function plotURA.

 plotURA(dataURA = dataURA[c(names(dataDual$TSG), names(dataDual$OCG)),],plotNAME = "URAplot")

The figure resulted from the code above is shown below:

Case study n. 2: Expression pipeline Pan Cancer 5 cancer types

cancerList <- c("BLCA","COAD","ESCA","HNSC","STAD")

listMoonlight <- moonlight(cancerType = cancerList, 
                      dataType = "Gene expression",
                      directory = "data",
                      nSample = 10,
                      nTF = 100,
                      DiffGenes = TRUE,
                      BPname = c("apoptosis","proliferation of cells"))
save(listMoonlight, file = paste0("listMoonlight_ncancer4.Rdata"))

plotCircos: Moonlight Circos Plot

The results of the moonlight pipeline can be visualized with a circos plot. Outer ring: color by cancer type, Inner ring: OCGs and TSGs, Inner connections: green: common OCGs yellow: common TSGs red: possible dual role

plotCircos(listMoonlight = listMoonlight, additionalFilename = "_ncancer5")

The figure resulted from the code above is shown below:

Case study n. 3: Downstream analysis BRCA with stages

setwd("/Users/antoniocolaprico/Dropbox/IB2_postdoc/Github/Moonlight/")
load("/Users/antoniocolaprico/Dropbox/IB2_postdoc/Github/PackageTesting/TCGAbiolinks/data/geneInfo.rda")
load("/Users/antoniocolaprico/Downloads/BRCAlistMoonlight_stages-2.RData")
require(MoonlightR)
require(TCGAbiolinks)



listMoonlight <- NULL
for (i in 1:4){
    dataDual <- moonlight(cancerType = "BRCA", 
                      dataType = "Gene expression",
                      directory = "data",
                      nSample = 10,
                      nTF = 100,
                      DiffGenes = TRUE,
                      BPname = c("apoptosis","proliferation of cells"),
                      stage = i)
    listMoonlight <- c(listMoonlight, list(dataDual))
    save(dataDual, file = paste0("dataDual_stage",as.roman(i), ".Rdata"))
}
names(listMoonlight) <- c("stage1", "stage2", "stage3", "stage4")

# Prepare mutation's data for stages

mutation <- GDCquery_Maf(tumor = "BRCA")


  dataClin <- GDCquery_clinic(project = "TCGA-BRCA",type = "clinical_patient")
  dataClin <- GDCquery_clinic(project = "TCGA-HNSC",type = "clinical_patient")              


res.mutation <- NULL
for(stage in 1:4){
  
  curStage <- paste0("Stage ", as.roman(stage))
                dataClin$tumor_stage <- toupper(dataClin$tumor_stage)
                dataClin$tumor_stage <- gsub("[ABCDEFGH]","",dataClin$tumor_stage)
                dataClin$tumor_stage <- gsub("ST","Stage",dataClin$tumor_stage)

                dataStg <- dataClin[dataClin$tumor_stage %in% curStage,]
                message(paste(curStage, "with", nrow(dataStg), "samples"))
dataSmTP <- mutation$Tumor_Sample_Barcode
                
                dataStgC <- dataSmTP[substr(dataSmTP,1,12) %in% dataStg$bcr_patient_barcode]
                dataSmTP <- dataStgC
  
                info.mutation <- mutation[mutation$Tumor_Sample_Barcode %in% dataSmTP,]
  
     ind <- which(info.mutation[,"Consequence"]=="inframe_deletion")
     ind2 <- which(info.mutation[,"Consequence"]=="inframe_insertion")
     ind3 <- which(info.mutation[,"Consequence"]=="missense_variant")
    res.mutation <- c(res.mutation, list(info.mutation[c(ind, ind2, ind3),c(1,51)]))
    }
names(res.mutation) <- c("stage1", "stage2", "stage3", "stage4")


tmp <- NULL
tmp <- c(tmp, list(listMoonlight[[1]][[1]]))
tmp <- c(tmp, list(listMoonlight[[2]][[1]]))
tmp <- c(tmp, list(listMoonlight[[3]][[1]]))
tmp <- c(tmp, list(listMoonlight[[4]][[1]]))
names(tmp) <- names(listMoonlight)

 mutation <- GDCquery_Maf(tumor = "BRCA")    

 plotCircos(listMoonlight=listMoonlight,listMutation=res.mutation, additionalFilename="proc2_wmutation", intensityColDual=0.2,fontSize = 2)

The results of the moonlight pipeline can be visualized with a circos plot. Outer ring: color by cancer type, Inner ring: OCGs and TSGs, Inner connections: green: common OCGs yellow: common TSGs red: possible dual role

The figure generated by the code above is shown below:

Moonlight working with molecular subtypes data.

In this section we showed downstream analysis with replication of Moonlight in TCGA’s data within comparison of different molecular subtypes and normal samples. You can use the function TCGAquery_subtype from TCGAbiolinks to retrieve this information.

The Cancer Genome Atlas (TCGA) Research Network has reported integrated genome-wide studies of various diseases. We have added some of the subtypes defined by these report in our package. The ACC(Cancer Genome Atlas Research Network and others 2016), BRCA (Cancer Genome Atlas Research Network and others 2012c), COAD (Cancer Genome Atlas Research Network and others 2012b), GBM (Ceccarelli, Michele and Barthel, Floris P and Malta, Tathiane M and Sabedot, Thais S and Salama, Sofie R and Murray, Bradley A and Morozova, Olena and Newton, Yulia and Radenbaugh, Amie and Pagnotta, Stefano M and others 2016), HNSC (Cancer Genome Atlas Research Network and others 2015a), KICH (Davis, Caleb F and Ricketts, Christopher J and Wang, Min and Yang, Lixing and Cherniack, Andrew D and Shen, Hui and Buhay, Christian and Kang, Hyojin and Kim, Sang Cheol and Fahey, Catherine C and others 2014), KIRC(Cancer Genome Atlas Research Network and others 2013a), KIRP (Linehan, W Marston and Spellman, Paul T and Ricketts, Christopher J and Creighton, Chad J and Fei, Suzanne S and Davis, Caleb and Wheeler, David A and Murray, Bradley A and Schmidt, Laura and Vocke, Cathy D and others 2016), LGG (Ceccarelli, Michele and Barthel, Floris P and Malta, Tathiane M and Sabedot, Thais S and Salama, Sofie R and Murray, Bradley A and Morozova, Olena and Newton, Yulia and Radenbaugh, Amie and Pagnotta, Stefano M and others 2016), LUAD (Cancer Genome Atlas Research Network and others 2014b), LUSC(Cancer Genome Atlas Research Network and others 2012a), PRAD(Cancer Genome Atlas Research Network and others 2015c), READ (Cancer Genome Atlas Research Network and others 2012b), SKCM (Cancer Genome Atlas Research Network and others 2015b), STAD (Cancer Genome Atlas Research Network and others 2014a), THCA (Cancer Genome Atlas Research Network and others 2014c), UCEC (Cancer Genome Atlas Research Network and others 2013b) tumors have data added.

A subset of the lgg subytpe is shown below:

## Subtype information from:doi:10.1016/j.cell.2015.12.028

Moonlight working with mutation data.

In this section we showed downstream analysis with replication of Moonlight in TCGA’s data within comparison of different molecular subtypes and normal samples. You can use the function GDCquery_Maf from TCGAbiolinks to retrieve this information.

A subset of the lgg subytpe is shown below:

## Subtype information from:doi:10.1016/j.cell.2015.12.028

Session Information ******

sessionInfo()

## R version 3.3.1 (2016-06-21)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.11.1 (El Capitan)
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] grid      parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
## [1] TCGAbiolinks_2.1.11 png_0.1-7           MoonlightR_0.99.1  
## [4] doParallel_1.0.10   iterators_1.0.8     foreach_1.4.3      
## [7] BiocStyle_2.1.33   
## 
## loaded via a namespace (and not attached):
##   [1] circlize_0.3.9                         
##   [2] fastmatch_1.0-4                        
##   [3] aroma.light_3.3.2                      
##   [4] plyr_1.8.4                             
##   [5] igraph_1.0.1                           
##   [6] ConsensusClusterPlus_1.37.0            
##   [7] splines_3.3.1                          
##   [8] BiocParallel_1.7.8                     
##   [9] TH.data_1.0-7                          
##  [10] GenomeInfoDb_1.9.13                    
##  [11] ggplot2_2.1.0                          
##  [12] digest_0.6.10                          
##  [13] BiocInstaller_1.23.9                   
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##  [15] GOSemSim_1.99.4                        
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##  [17] gdata_2.17.0                           
##  [18] magrittr_1.5                           
##  [19] memoise_1.0.0                          
##  [20] cluster_2.0.4                          
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